{ "abstract": "Background\n\nEarly detection and monitoring of wound-related complications after total knee arthroplasty (TKA) are critical to optimize outcomes and preserve implants. Conventional postoperative monitoring relies on in-person assessment and patient-reported symptoms, which may delay recognition. Noncontact imaging acquired in the clinic or at home offers a potential route to earlier and more consistent assessment.Objective\n\nThis study aimed to evaluate the technical feasibility and performance of a multimodal AI system integrating electro-optical (EO) and infrared imaging for postoperative wound assessment following TKA and to explore clinical factors associated with early complications.Methods\n\nWe conducted a single-center prospective cohort study of 749 patients undergoing primary TKA. Patients underwent standardized multimodal data collection at scheduled postoperative visits. EO-based models localized surgical landmarks and segmented complication-oriented tissue states; a fine-tuned vision-language model provided protocol verification and contextual visual reasoning. EO landmarks mapped to thermal space via deterministic alignment, followed by infrared region-of-interest segmentation and thermal classification. Each module was evaluated on a defined held-out set. The thermal classifier was assessed out-of-fold under 5-fold cross-validation with folds grouped by patient, in knees imaged on postoperative day 14 or later. Discrimination for clinician-adjudicated infection-related complications served as the primary outcome. Comorbidities and baseline laboratory values were explored for associations with complications.Results\n\nAmong 749 patients, 26 (3.5%) had a clinician-adjudicated infection-related complication. On held-out evaluation, the EO landmark detector reached a sensitivity of 0.967 (95% CI 0.954-0.979) and positive predictive value of 0.955 (95% CI 0.940-0.970), and the wound-tissue segmentation model reached a sensitivity of 0.845 (95% CI 0.833-0.857) and positive predictive value of 0.759 (95% CI 0.744-0.772). Infrared region-of-interest extraction isolated the surgical field with a mean intersection-over-union of 0.985 (SD 0.008; 95% CI 0.984-0.986) across 178 held-out patients. The trained thermal classifier discriminated infected scans with an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI 0.959-0.992) in out-of-fold, patient-grouped cross-validation. At a representative operating point, sensitivity was 0.808 (95% CI 0.65-0.92), specificity 0.974 (95% CI 0.96-0.99), positive predictive value 0.618 (95% CI 0.49-0.78), and negative predictive value 0.990 (95% CI 0.98-1.00). Exploratory analyses showed nominal associations of chronic kidney disease and diabetes mellitus with postoperative complications that did not remain significant after multiplicity correction.Conclusions\n\nIn this formative study, a multimodal EO and infrared imaging pipeline demonstrated technical feasibility for postoperative wound assessment following TKA, providing noncontact discrimination of infection-related complications that compared favorably with established adjuncts. This work provides proof-of-concept for multimodal imaging-based assessment and defines the technical requirements for the prospective, multicenter external validation needed to establish clinical utility.", "abstract_shown": true, "affiliations": [ "Truss Health, Inc., Las Vegas, NV, United States.", "Sunshine Bone and Joint Institute, Krishna Institute of Medical Sciences, Begumpet, Telangana, India." ], "article_type": "research article", "arxiv": null, "author_total": 7, "authors": [ { "affs": [ 0 ], "name": "Tej S Ranade", "orcid": "0009-0009-4211-937X" }, { "affs": [ 0 ], "name": "Emily Hu", "orcid": "0000-0002-5262-8217" }, { "affs": [ 0 ], "name": "Kadambari Beelwar", "orcid": "0009-0008-2419-5793" }, { "affs": [ 1 ], "name": "Sandeep Boddeda", "orcid": "0000-0001-5750-3221" }, { "affs": [ 1 ], "name": "Praharsha Mulpur", "orcid": "0000-0002-1659-2587" }, { "affs": [ 1 ], "name": "Adarsh Annapareddy", "orcid": "0000-0001-9463-8950" }, { "affs": [ 1 ], "name": "A V Gurava Reddy", "orcid": "0000-0003-2207-8004" } ], "bibtex": "https://research.draconitic.com/paper/c2m6e8ppyv5fh.bib", "cited_by": 0, "doi": "10.2196/99244", "fulltext_url": "https://europepmc.org/articles/PMC13691468", "id": "c2m6e8ppyv5fh", "issns": [ "2561-326X" ], "landing_url": "https://europepmc.org/article/MED/42849894", "language": "eng", "licence": "CC-BY", "licence_label": "CC BY", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2741|Infrared Thermography in Medicine" ] ], "openalex": "W7220899899", "pdf_url": "https://europepmc.org/articles/PMC13691468?pdf=render", "pmcid": "PMC13691468", "pmid": "42849894", "published": "2026-10-08", "publisher": null, "ris": "https://research.draconitic.com/paper/c2m6e8ppyv5fh.ris", "sources": [ { "first_seen": "2026-10-30T12:18:02Z", "native_id": "MED:42849894", "source": "europepmc", "url": "https://europepmc.org/article/MED/42849894" } ], "subjects": [ { "field": "medicine", "origin": "openalex:2741|Infrared Thermography in Medicine", "subfield": "imaging" } ], "title": "Technical Feasibility of a Multimodal Electro-Optical and Infrared Imaging Pipeline for Postoperative Wound Assessment After Total Knee Arthroplasty: Formative Evaluation Study.", "title_text": "Technical Feasibility of a Multimodal Electro-Optical and Infrared Imaging Pipeline for Postoperative Wound Assessment After Total Knee Arthroplasty: Formative Evaluation Study.", "type": "peer-reviewed", "updated_at": "2026-10-30T12:18:02Z", "url": "https://research.draconitic.com/paper/c2m6e8ppyv5fh", "venue": "JMIR formative research", "versions": [] }